This paper reports the development of a practical visible light positioning (VLP) system using received signal strength. The indoor localization system is accurate and easy to train and calibrate despite using fingerprinting technique. The VLP system consists of cheap photodiode-based receiver and consumer grade LED luminaires. The impact of distance metrics used to compute the weights of the weighted K-nearest neighbor (WKNN) algorithm on the localization accuracy of the VLP is investigated. Experimental results show that square chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric. A room-scale implementation shows that a mean error of 2.2 cm and a 90-percentile error of 4.9 cm within a 3.3 m × 2.1 m 2-D floor space are achievable. However, the high localization accuracy comes at the cost of requiring 187 offline measurements to construct the fingerprint database. A method for estimating an optical propagation model using only a handful of measurements is developed to address this problem. This leads to the creation of a dense and accurate fingerprinting database through fabricated data. The performance of the VLP system does not degrade noticeably when the localization is performed with the fabricated data. A mean error of 2.7 cm and a 90-percentile error of 5.7 cm are achievable with only 12 offline measurements.

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field.

The characterization of three types of novel planar electromagnetic sensors: 1) meander; 2) mesh; and 3) interdigital configuration, has been studied and their comparative performance has been evaluated based on their areas of applications. All of them are suitable for inspection and evaluation of system properties without destroying them. The experiments on fabricated sensors have been conducted and the results are presented here. The target application is to use a mixture of different types of sensors to detect plastic landmines.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

This paper reports on the development of an ultrasonic sensing-based active localization system. The system employs an ultrasonic array to transmit chirp signals and time-of-flight measurement for ranging. The position of the receiver is estimated iteratively using the spring-relaxation technique. A median and 90-percentile error of 12.4 and 21.7 mm, respectively, were obtained for measurements at 625 positions within a 1.2 m × 1.2 m area testbed. The spring-relaxation technique outperforms the widely adopted linear least square-based lateration technique while using the same ranging data. The performance of the system is benchmarked against that of visible light positioning using the same platform setup. The reported results show the ultrasonic system to be more accurate when compared with the visible light positioning system, which achieved median and 90-percentile errors of 33.7 and 58.6 mm, respectively.